A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery


Avci M., Topaloglu Ş. A.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.53, ss.160-171, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 53
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.eswa.2016.01.038
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.160-171
  • Anahtar Kelimeler: Vehicle routing, Heterogeneous fleet, Local search, Reverse logistics, TABU SEARCH ALGORITHM, HEURISTIC ALGORITHMS, TIME WINDOWS, EVOLUTIONARY ALGORITHM, FLEET SIZE, OPTIMIZATION, SERVICE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a variant of the classical Vehicle Routing Problem (VRP) where the vehicles serve a set of customers demanding pickup and delivery services at the same time. The VRPSPD can arise in many transportation systems involving both distribution and collection operations. Originally, the VRPSPD assumes a homogeneous fleet of vehicles to serve the customers. However, in many practical situations, there are different types of vehicles available to perform the pickup and delivery operations. In this study, the original version of the VRPSPD is extended by assuming the fleet of vehicles to be heterogeneous. The Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery (HVRPSPD) is considered to be an NP-hard problem because it generalizes the classical VRP. For its solution, we develop a hybrid focal search algorithm in which a non-monotone threshold adjusting strategy is integrated with tabu search. The threshold function used in the algorithm has an adaptive nature which makes it self-tuning. Additionally, its implementation is very simple as it requires no parameter tuning except for the tabu list length. The proposed algorithm is applied to a set of randomly generated problem instances. The results indicate that the developed approach can produce efficient and effective solutions. (c) 2016 Elsevier Ltd. All rights reserved.